IoT Blockchain Detection for High-Scale DDoS Countermeasures
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Solution Overview
Problem
Existing DDoS detection and mitigation mechanisms for mobile phone networks are inadequate to handle the pace and scale of automated IoT DDoS attacks due to the high density and lack of user interfaces in IoT devices, necessitating intelligent, automated network DDoS detection and mitigation.
Innovation Solution
A method involving machine learning and blockchain technologies to identify and respond to DDoS attacks by analyzing traffic data, allocating dedicated frequency channels, and using decentralized ledgers to record and countermeasure anomalous IoT UE activity, enabling early detection and localization of attacks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If legacy DDoS detection mechanisms for mobile phone networks are used, then existing infrastructure can be utilized, but they are unable to handle the pace and scale of automated IoT DDoS attacks
Solution Approach 1:
The patent transforms legacy mobile network DDoS detection mechanisms into IoT-capable systems by changing key parameters: implementing automated anomaly detection algorithms, deploying distributed ledger technology for device identification, and adjusting detection thresholds to accommodate IoT traffic patterns. This allows existing infrastructure to reliably detect automated IoT DDoS attacks without complete system replacement.
2Quantity of substance
If high device density is implemented in IoT networks, then network coverage and connectivity are improved, but the risk and scale of DDoS attacks increase
Solution Approach 1:
The patent implements continuous feedback loops where network nodes monitor traffic patterns from high-density IoT devices, compare against baseline behavior, and automatically trigger countermeasures when anomalies indicate DDoS attacks. This feedback mechanism enables the network to maintain high device density while dynamically responding to and mitigating attack risks in real-time.
Solution Approach 2:
The patent introduces distributed ledger technology as an intermediary layer between high-density IoT devices and the network core. This intermediary automatically identifies compromised devices, records their behavior, and coordinates countermeasures, allowing the network to harness the connectivity benefits of high device density while filtering out malicious traffic before it reaches critical infrastructure.
3Extent of automation
If automated device management platforms are deployed to manage large numbers of IoT devices, then device oversight and control are enhanced, but a single compromised platform can rapidly deploy malicious software to massive numbers of devices
Solution Approach 1:
The patent segments the automated device management function across multiple distributed nodes rather than relying on a single centralized platform. Each node independently manages subsets of IoT devices and maintains its own security posture, so that compromise of one node cannot lead to widespread malicious software deployment. This segmentation preserves automation benefits while limiting the blast radius of potential compromises.
Solution Approach 2:
The patent introduces blockchain-based verification as an intermediary layer between automated management platforms and IoT devices. This intermediary cryptographically verifies software updates and management commands, preventing compromised platforms from deploying malicious software even if automation is exploited. The distributed ledger maintains trustless verification across the automated management ecosystem.
Data Source
AI summary
Distributed denial of service (DDoS) attacks launched using internet of things (IoT) devices may be detected and addressed using decentralized computing, potentially in combination with blockchain or other decentralized ledger technology. One method of doing this may include identifying an anomaly by comparing traffic data against baseline traffic data, reporting an anomaly as a potential attack, receiving an indication that the anomaly is an attack, and then performing a set of response measures. The set of response measures may comprise allocating a dedicated frequency channel to traffic associated with the attack and assigning devices associated with the attack to that channel, while assigning other user devices to different frequency channels. Records of the attack and associated devices may be recorded and propagated across nodes, thereby enabling each node to respond appropriately even as the device moves from its original location. Machine learning can be implemented to improve the analysis of key performance indicators to determine which indicators are most predictive of DDoS attacks.